For each video, we sample 16 key frames from each video. For each key frame, 32 bounding boxes are generated from Faster R-CNN [1]} built on ResNet-101 [2]} pre-trained on Visual Genomes [3]}. For each detected region of the interest (ROI), i.e., the bounding box, its feature is obtained by sum-pooling the convolutional features within the bounding box. The feature dimension is 2048. We use k-means to group \(16\times 32 = 512\)  detected bounding boxes into 32 clusters and use the cluster centroids as the video's initial representation. We set the number of head in combo-attention network as 8. The tree contains 8 million nodes in total, among which, 4 million are leaf nodes. All models are trained and deployed based on the PaddlePaddle deep learning framework developed by Baidu.
